IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/w85z2.html
   My bibliography  Save this paper

Housing-Price Prediction in Colombia using Machine Learning

Author

Listed:
  • Otero Gomez, Daniel
  • MANRIQUE, MIGUEL ANGEL CORREA
  • Sierra, Omar Becerra
  • Laniado, Henry
  • Mateus C, Rafael
  • Millan, David Andres Romero

Abstract

It is a common practice to price a house without proper evaluation studies being performed for assurance. That is why the purpose of this study provide an explanatory model by establishing parameters for accuracy in interpretation and projection of housing prices. In addition, it is intentioned to establish proper data preprocessing practices in order to increase the accuracy of machine learning algorithms. Indeed, according to our literature review, there are few articles and reports on the use of Machine Learning tools for the prediction of property prices in Colombia. The dataset in which the research is built upon was provided by an existing real estate company. It contains near 940,000 items (housing advertisements) posted on the platform from the year 2018 to 2020. The database was enriched using statistical imputation techniques. Housing prices prediction was performed using Decision Tree Regressors and LightGBM methods, thus deriving in better alternatives for house price prediction in Colombia. Moreover, to measure the accuracy of the proposed models, the Root Mean Squared Logarithmic Error (RMSLE) statistical indicator was used. The best cross validation results obtained were 0.25354±0.00699 for the LightGBM, 0.25296 ±0.00511 for the Bagging Regressor, and 0.25312±0.00559 for the ExtraTree Regressor with Bagging Regressor, and it was not found a statistical difference between their performances.

Suggested Citation

  • Otero Gomez, Daniel & MANRIQUE, MIGUEL ANGEL CORREA & Sierra, Omar Becerra & Laniado, Henry & Mateus C, Rafael & Millan, David Andres Romero, 2020. "Housing-Price Prediction in Colombia using Machine Learning," OSF Preprints w85z2, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:w85z2
    DOI: 10.31219/osf.io/w85z2
    as

    Download full text from publisher

    File URL: https://osf.io/download/613f7b42079a0a055001a6db/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/w85z2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:w85z2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.